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          <h1 class="post-title" itemprop="name headline">pandas基础</h1>
        

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        <h2 id="pandas数据类型"><a href="#pandas数据类型" class="headerlink" title="pandas数据类型"></a>pandas数据类型</h2><h3 id="Series"><a href="#Series" class="headerlink" title="Series"></a>Series</h3><p>Series 是 Pandas 中最基本的一维数组形式。其可以储存整数、浮点数、字符串等类型的数据</p>
<p>基本结构<code>pandas.Series(data=None, index=None)</code></p>
<p>其中，data 可以是字典，或者NumPy 里的 ndarray 对象等。index 是数据索引，索引是 Pandas 数据结构中的一大特性，它主要的功能是帮助我们更快速地定位数据。</p>
<h4 id="创建一个series"><a href="#创建一个series" class="headerlink" title="创建一个series"></a>创建一个series</h4><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"></span><br><span class="line">s= pd.Series(&#123;<span class="string">'a'</span>:<span class="number">10</span>,<span class="string">'b'</span>:<span class="number">20</span>,<span class="string">'c'</span>:<span class="number">30</span>&#125;)</span><br></pre></td></tr></table></figure>
<p>如果series是从array转换过去，那么索引默认是array的下标 </p>
<h3 id="DataFrame"><a href="#DataFrame" class="headerlink" title="DataFrame"></a>DataFrame</h3><p>DataFrame 是 Pandas 中最为常见、最重要且使用频率最高的数据结构。DataFrame 和平常的电子表格或 SQL 表结构相似。你可以把 DataFrame 看成是 Series 的扩展类型，它仿佛是由多个 Series 拼合而成。它和 Series 的直观区别在于，数据不但具有行索引，且具有列索引。</p>
<p><img width="400px" src="https://doc.shiyanlou.com/courses/uid214893-20190531-1559284057250"></p>
<p>基本结构：<code>pandas.DataFrame(data=None, index=None, columns=None)</code></p>
<p>就是在Series的基础上增加了columns列索引</p>
<h4 id="创建一个DataFrame"><a href="#创建一个DataFrame" class="headerlink" title="创建一个DataFrame"></a>创建一个DataFrame</h4><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br></pre></td><td class="code"><pre><span class="line">df = pd.DataFrame(&#123;<span class="string">'one'</span>: pd.Series([<span class="number">1</span>, <span class="number">2</span>, <span class="number">3</span>]),</span><br><span class="line">                   <span class="string">'two'</span>: pd.Series([<span class="number">4</span>, <span class="number">5</span>, <span class="number">6</span>])&#125;)</span><br><span class="line"></span><br><span class="line">df = pd.DataFrame(&#123;<span class="string">'one'</span>: [<span class="number">1</span>, <span class="number">2</span>, <span class="number">3</span>],</span><br><span class="line">                   <span class="string">'two'</span>: [<span class="number">4</span>, <span class="number">5</span>, <span class="number">6</span>]&#125;)</span><br><span class="line"></span><br><span class="line">df = pd.DataFrame([&#123;<span class="string">'one'</span>: <span class="number">1</span>, <span class="string">'two'</span>: <span class="number">4</span>&#125;,</span><br><span class="line">                   &#123;<span class="string">'one'</span>: <span class="number">2</span>, <span class="string">'two'</span>: <span class="number">5</span>&#125;,</span><br><span class="line">                   &#123;<span class="string">'one'</span>: <span class="number">3</span>, <span class="string">'two'</span>: <span class="number">6</span>&#125;])</span><br></pre></td></tr></table></figure>
<h2 id="数据读取"><a href="#数据读取" class="headerlink" title="数据读取"></a>数据读取</h2><h3 id="读取CSV文件"><a href="#读取CSV文件" class="headerlink" title="读取CSV文件"></a>读取CSV文件</h3><p>方法：<code>pandas.read_csv()</code></p>
<p>pd.read_ 前缀开始的方法还可以读取各式各样的数据文件，且支持连接数据库</p>
<h2 id="基本操作"><a href="#基本操作" class="headerlink" title="基本操作"></a>基本操作</h2><h3 id="数据预览"><a href="#数据预览" class="headerlink" title="数据预览"></a>数据预览</h3><p>有些时候，我们读取的文件很大。如果全部输出预览这些文件，既不美观，又很耗时。还好，Pandas 提供了 <code>head()</code> 和 <code>tail()</code> 方法，它可以帮助我们只预览一小块数据。</p>
<h3 id="统计性描述"><a href="#统计性描述" class="headerlink" title="统计性描述"></a>统计性描述</h3><p>Pandas 还提供了统计和描述性方法，方便你从宏观的角度去了解数据集。<code>describe()</code> 相当于对数据集进行概览，会输出该数据集每一列数据的计数、最大值、最小值等。</p>
<h3 id="与numpy之间转换"><a href="#与numpy之间转换" class="headerlink" title="与numpy之间转换"></a>与numpy之间转换</h3><p><code>.values</code> 可以将 DataFrame 转换为 NumPy 数组。</p>
<h2 id="数据选择"><a href="#数据选择" class="headerlink" title="数据选择"></a>数据选择</h2><h3 id="基于索引数字选择"><a href="#基于索引数字选择" class="headerlink" title="基于索引数字选择"></a>基于索引数字选择</h3><p>方法：<code>.iloc()</code></p>
<p>该方法可以接受的类型有：</p>
<ol>
<li>整数。例如：<code>5</code></li>
<li>整数构成的列表或数组。例如：<code>[1, 2, 3]</code></li>
<li>布尔数组。</li>
<li>可返回索引值的函数或参数。</li>
</ol>
<h3 id="基于标签名称选择"><a href="#基于标签名称选择" class="headerlink" title="基于标签名称选择"></a>基于标签名称选择</h3><p>方法：<code>.loc()</code></p>
<p>可以接受的类型有：</p>
<ol>
<li>单个标签。例如：<code>2</code> 或 <code>&#39;a&#39;</code>，这里的 <code>2</code> 指的是标签而不是索引位置。</li>
<li>列表或数组包含的标签。例如：<code>[&#39;A&#39;, &#39;B&#39;, &#39;C&#39;]</code>。</li>
<li>切片对象。例如：<code>&#39;A&#39;:&#39;E&#39;</code>，注意这里和上面切片的不同支持，首尾都包含在内。</li>
<li>布尔数组。</li>
<li>可返回标签的函数或参数。</li>
</ol>
<h2 id="数据增删"><a href="#数据增删" class="headerlink" title="数据增删"></a>数据增删</h2><p>方法：<code>.drop()</code></p>
<p>pandas.DataFrame.drop) 可以直接去掉数据集中指定的列和行。一般在使用时，我们指定 <code>labels</code> 标签参数，然后再通过 <code>axis</code> 指定按列或按行删除即可。当然，你也可以通过索引参数删除数据，具体查看官方文档。</p>
<p><code>df.drop(labels=[&#39;a&#39;, &#39;b&#39;], axis=1)</code></p>
<p><code>DataFrame.drop_duplicates</code> 则通常用于数据去重，即剔除数据集中的重复值。使用方法非常简单，指定去除重复值规则，以及 <code>axis</code> 按列还是按行去除即可。</p>
<p>除此之外，另一个用于数据删减的方法 <code>DataFrame.dropna</code> 也十分常用，其主要的用途是删除缺少值，即数据集中空缺的数据列或行。</p>
<h2 id="数据填充"><a href="#数据填充" class="headerlink" title="数据填充"></a>数据填充</h2><h3 id="检测缺失值"><a href="#检测缺失值" class="headerlink" title="检测缺失值"></a>检测缺失值</h3><p>Pandas 为了更方便地检测缺失值，将不同类型数据的缺失均采用 <code>NaN</code> 标记。这里的 NaN 代表 Not a Number，它仅仅是作为一个标记。例外是，在时间序列里，时间戳的丢失采用 <code>NaT</code> 标记。</p>
<p>Pandas 中用于检测缺失值主要用到两个方法，分别是：<code>isna()</code> 和 <code>notna()</code>，顾名思义就是「是缺失值」和「不是缺失值」。默认会返回布尔值用于判断。</p>
<h3 id="填充"><a href="#填充" class="headerlink" title="填充"></a>填充</h3><p>方法：<code>.fillna()</code> </p>
<p>用相同的标量值替换NaN</p>
<h3 id="插值填充"><a href="#插值填充" class="headerlink" title="插值填充"></a>插值填充</h3><p>我们可以通过 <code>interpolate()</code> 方法完成线性插值。</p>
<p>对于 interpolate() 支持的插值算法，也就是 method=。下面给出几条选择的建议：</p>
<p>如果你的数据增长速率越来越快，可以选择 method=’quadratic’二次插值。</p>
<p>如果数据集呈现出累计分布的样子，推荐选择 method=’pchip’。</p>
<p>如果需要填补缺省值，以平滑绘图为目标，推荐选择 method=’akima’。</p>
<p>当然，最后提到的 method=’akima’，需要你的环境中安装了 Scipy 库。除此之外，method=’barycentric’ 和 method=’pchip’ 同样也需要 Scipy 才能使用。</p>
<h2 id="数据可视化"><a href="#数据可视化" class="headerlink" title="数据可视化"></a>数据可视化</h2><p>方法：<code>DataFrame.plot()</code> </p>
<p>可以绘制多种样式 指定kind=参数即可</p>
<p><code>DataFrame.plot(kind=&#39;bar&#39;)</code></p>
<h2 id="pandas时间序列"><a href="#pandas时间序列" class="headerlink" title="pandas时间序列"></a>pandas时间序列</h2><h3 id="生成时间序列"><a href="#生成时间序列" class="headerlink" title="生成时间序列"></a>生成时间序列</h3><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 30天为间隔</span></span><br><span class="line">dr = pd.date_range(start=<span class="string">"20180101"</span>,end=<span class="string">"20200802"</span>,freq=<span class="string">"30D"</span>)</span><br><span class="line"><span class="comment"># 生成20个</span></span><br><span class="line">dr2 = pd.date_range(start=<span class="string">"20180101"</span>,end=<span class="string">"20200802"</span>,periods=<span class="number">20</span>)</span><br></pre></td></tr></table></figure>
      
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              <div class="post-toc-content"><ol class="nav"><li class="nav-item nav-level-2"><a class="nav-link" href="#pandas数据类型"><span class="nav-number">1.</span> <span class="nav-text">pandas数据类型</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#Series"><span class="nav-number">1.1.</span> <span class="nav-text">Series</span></a><ol class="nav-child"><li class="nav-item nav-level-4"><a class="nav-link" href="#创建一个series"><span class="nav-number">1.1.1.</span> <span class="nav-text">创建一个series</span></a></li></ol></li><li class="nav-item nav-level-3"><a class="nav-link" href="#DataFrame"><span class="nav-number">1.2.</span> <span class="nav-text">DataFrame</span></a><ol class="nav-child"><li class="nav-item nav-level-4"><a class="nav-link" href="#创建一个DataFrame"><span class="nav-number">1.2.1.</span> <span class="nav-text">创建一个DataFrame</span></a></li></ol></li></ol></li><li class="nav-item nav-level-2"><a class="nav-link" href="#数据读取"><span class="nav-number">2.</span> <span class="nav-text">数据读取</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#读取CSV文件"><span class="nav-number">2.1.</span> <span class="nav-text">读取CSV文件</span></a></li></ol></li><li class="nav-item nav-level-2"><a class="nav-link" href="#基本操作"><span class="nav-number">3.</span> <span class="nav-text">基本操作</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#数据预览"><span class="nav-number">3.1.</span> <span class="nav-text">数据预览</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#统计性描述"><span class="nav-number">3.2.</span> <span class="nav-text">统计性描述</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#与numpy之间转换"><span class="nav-number">3.3.</span> <span class="nav-text">与numpy之间转换</span></a></li></ol></li><li class="nav-item nav-level-2"><a class="nav-link" href="#数据选择"><span class="nav-number">4.</span> <span class="nav-text">数据选择</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#基于索引数字选择"><span class="nav-number">4.1.</span> <span class="nav-text">基于索引数字选择</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#基于标签名称选择"><span class="nav-number">4.2.</span> <span class="nav-text">基于标签名称选择</span></a></li></ol></li><li class="nav-item nav-level-2"><a class="nav-link" href="#数据增删"><span class="nav-number">5.</span> <span class="nav-text">数据增删</span></a></li><li class="nav-item nav-level-2"><a class="nav-link" href="#数据填充"><span class="nav-number">6.</span> <span class="nav-text">数据填充</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#检测缺失值"><span class="nav-number">6.1.</span> <span class="nav-text">检测缺失值</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#填充"><span class="nav-number">6.2.</span> <span class="nav-text">填充</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#插值填充"><span class="nav-number">6.3.</span> <span class="nav-text">插值填充</span></a></li></ol></li><li class="nav-item nav-level-2"><a class="nav-link" href="#数据可视化"><span class="nav-number">7.</span> <span class="nav-text">数据可视化</span></a></li><li class="nav-item nav-level-2"><a class="nav-link" href="#pandas时间序列"><span class="nav-number">8.</span> <span class="nav-text">pandas时间序列</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#生成时间序列"><span class="nav-number">8.1.</span> <span class="nav-text">生成时间序列</span></a></li></ol></li></ol></div>
            

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